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Landsat-5 TM and Lidar Fusion for Sub-Pixel Juniper Tree Cover Estimates in a Western Rangeland
Photogrammetric Engineering & Remote Sensing (2011)
  • Temuulen Sankey, Idaho State University
  • Nancy Glenn, Idaho State University
Pinyon-juniper woodlands comprise the third most common land-cover type in the United States and have been documented to have drastically increased both in density and extent in recent decades. We explored Landsat-5 TM and Light Detection and Ranging (lidar) data, individually and fused together, for estimating sub-pixel juniper cover. Linear spectral unmixing (LSU), Constrained Energy Minimization (CEM), and Mixture Tuned Matched Filtering (MTMF) techniques were compared along with spectral-lidar fusion approaches. None of the Landsat-5 TM-derived estimates were significantly correlated with field-measured juniper cover (n = 100), while lidar-derived estimates were strongly correlated (R2 = 0.74, p-value <0.001). Fusion of these estimates produced superior results to both classifications individually (R2 = 0.80, p-value <0.001). The MTMF technique performed best, while a multiple regression-based fusion was the best approach to combining the two data sources. Future studies can use the best sub-pixel classification and fusion approach to quantify changes in associated ecosystem properties such as carbon.
Publication Date
December, 2011
Citation Information
Temuulen Sankey and Nancy Glenn. "Landsat-5 TM and Lidar Fusion for Sub-Pixel Juniper Tree Cover Estimates in a Western Rangeland" Photogrammetric Engineering & Remote Sensing Vol. 77 Iss. 12 (2011)
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